
Proof of theorem...
It's the conditional Bayes theorem:
Some conditions, blah blah
Then $\displaystyle \bar{E}[X\mathcal{G}] = \frac{E[\Lambda X\mathcal{G}]}{E[\Lambda\mathcal{G}]}$
I'm fine with the bulk of the proof, it's just that it starts off by defining
$\displaystyle Y=\frac{E[\Lambda X\mathcal{G}]}{E[\Lambda\mathcal{G}]}$ if $\displaystyle E[\Lambda\mathcal{G}]>0$ and Y = 0 otherwise
Then it says we need to show $\displaystyle Y = \bar{E}[X\mathcal{G}]$
But I don't really see that $\displaystyle Y=\frac{E[\Lambda X\mathcal{G}]}{E[\Lambda\mathcal{G}]}$
In fact if $\displaystyle E[\Lambda\mathcal{G}] = 0$ Then the RHS of the formula is undefined! So.... what's going on here? :S
Thanks for any help!
